Salīdzināt metodes

	Krombaha alfa koeficienta vietā Makdonalda omega (ω)	Korelatīvās faktoru analīzes (KFA)	Apstiprinošā faktoru analīze (AFA)
Nozare	Psihometrija	Statistika	Psihometrija
Saime	Latent structure	Latent structure	Latent structure
Izcelsmes gads	1999	1969	1969
Autors	Roderick P. McDonald	Karl Jöreskog	Karl Gustav Jöreskog
Tips	Reliability coefficient / latent variable model	Confirmatory latent variable model	Hypothesis-testing latent variable model
Pirmavots	McDonald, R. P. (1999). Test Theory: A Unified Treatment. Lawrence Erlbaum Associates. ISBN: 978-0805830750	Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363	Jöreskog, K. G. (1969). A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34(2), 183–202.
Citi nosaukumi	omega reliability, ω coefficient, omega total, omega hierarchical	Doğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement model	CFA, confirmatory FA, measurement model, restricted factor analysis
Saistītās	6	4	4
Kopsavilkums	McDonald's omega is a factor-analysis-based reliability coefficient introduced by Roderick P. McDonald (1999) that quantifies the internal consistency of a composite score without requiring the restrictive assumption that all items contribute equally to the latent factor. It yields two complementary indices: ω_total, which captures overall reliability of the sum score, and ω_hierarchical (ωh), which reports how much of the composite's variance is explained specifically by a single general factor.	Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.	Confirmatory factor analysis tests a researcher-specified factor structure against observed data. Unlike exploratory approaches, the researcher decides in advance which indicators load on which latent factor, and the model is evaluated by how closely the implied covariance matrix reproduces the sample covariance matrix. CFA is central to scale validation, construct validity assessment, and measurement invariance testing.